Electro-optical synthetic civilian vehicle data domes

Price, Rebecca L.; Ramirez, Juan Marcos; Rovito, Todd V.; Mendoza-Schrock, Olga

doi:10.1109/naecon.2012.6531044

Cited by 5 publications

(4 citation statements)

References 2 publications

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“…TheElectro-Optical(EO)SyntheticCivilianVehicleDataDomes(EO-SCVDD) dataset is maintained by the Air Force Research Laboratory (AFRL) Sensors Directorate and presented in (Price, Ramirez, Rovito & Mendoza-Schrock, 2012). Thevehiclesinthisdatasetarederivedfromthree-dimensional(3D)pointcloudsof physicallyaccuratevehiclemodels.Hencealthoughthedataissynthetic,themodels used for the vehicles were all derived from physically accurate dimensions.…”

Section: Methodsmentioning

confidence: 99%

Manifold Transfer Subspace Learning (MTSL) for Applications in Aided Target Recognition

Mendoza-Schrock

Rizki

Velten

2017

International Journal of Monitoring and Surveillance Technologies Research

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This article describes how transfer subspace learning has recently gained popularity for its ability to perform cross-dataset and cross-domain object recognition. The ability to leverage existing data without the need for additional data collections is attractive for monitoring and surveillance technology, specifically for aided target recognition applications. Transfer subspace learning enables the incorporation of sparse and dynamically collected data into existing systems that utilize large databases. Manifold learning has also gained popularity for its success at dimensionality reduction. In this contribution, Manifold learning and transfer subspace learning are combined to create a new system capable of achieving high target recognition rates. The manifold learning technique used in this contribution is diffusion maps, a nonlinear dimensionality reduction technique based on a heat diffusion analogy. The transfer subspace learning technique used is Transfer Fisher's Linear Discriminative Analysis. The new system, manifold transfer subspace learning, sequentially integrates manifold learning and transfer subspace learning. In this article, the ability of the new techniques to achieve high target recognition rates for cross-dataset and cross-domain applications is illustrated using a variety of diverse datasets.

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Section: Methodsmentioning

confidence: 99%

Manifold Transfer Subspace Learning (MTSL) for Applications in Aided Target Recognition

Mendoza-Schrock

Rizki

Velten

2017

International Journal of Monitoring and Surveillance Technologies Research

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“…The data used in this research is a preliminary version of the Electro-Optical Synthetic Civilian Vehicle Data Domes project [12]. Price et al (2012) introduce and describe this data set as a densely sampled multi-vehicle data set designed for pattern recognition tasks.…”

Section: Electro-optical Synthetic Vehicle Datamentioning

confidence: 99%

“…Price et al (2012) introduce and describe this data set as a densely sampled multi-vehicle data set designed for pattern recognition tasks. This data set was synthetically generated from scale models of different vehicle classes.…”

Section: Electro-optical Synthetic Vehicle Datamentioning

confidence: 99%

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Diffusion maps for exploring electro-optical synthetic vehicle image data

Ramirez

Mendoza-Schrock

2012

2012 IEEE National Aerospace and Electronics Conference (NAECON)

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In this work, we explore low-dimensional representations of high-dimensional data derived from electro-optical synthetic vehicle images. The collection of vehicle images consists of four different vehicle models: Toyota Camry, Toyota Avalon, Toyota Tacoma, and Nissan Sentra. This data contains 3,601 160 × 213 gray-scale vehicle images sampled uniformly over a camera view hemisphere. We use the non-linear manifold learning technique of diffusion maps with Gaussian kernel to explore low-dimensional structure the high-dimensional cloud of vehicle image observations. Diffusion maps have been shown to be a valuable tool in the analysis of high-dimensional data and the technique is able to extract an approximation for the underlying structure inherent to the data. Our analysis includes examining how the diffusion time and kernel width leads to different lowdimensional representations and we present a novel technique to relate the kernel width to the distribution of data in the observation space. In addition, we present initial results for multiclass vehicle classification through low-dimensional embedding coordinates and the out-of-sample extension of unlabeled vehicle images.

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